Min Lu
Price bubbles in Beijing carbon market and environmental policy announcement
Lu, Min; Wang, Xing; Speeckaert, Rosalie
Authors
Dr Xing Wang xing.wang@durham.ac.uk
Assistant Professor
Rosalie Speeckaert rosalie.c.speeckaert@durham.ac.uk
PGR Student Doctor of Philosophy
Abstract
This paper examines price bubbles in the relatively new carbon emission trading scheme of Beijing carbon market by employing a recently proposed econometric test which can stamp the occurrence and burst of financial bubbles. We find multiple bubbles in Beijing carbon market over the sample period between January 2014 to April 2018, and that the occurrences of carbon price bubbles are closely related to the announcements of environmental policies by the Chinese government. Comparing our results to the EU ETS, we find that the volatility of carbon price in Beijing market is higher than EU, and interestingly, the bubbles in Beijing market occur when the price volatility is relatively low, while in EU market the bubbles correspond to the peaks of volatility. Our empirical results provide insightful policy implications in the context of the actual China’s carbon market reform. To achieve effective stabilization of carbon price, policymakers should publicize alert notifications of the price fluctuations, and strengthen the carbon markets supervision and promote its improvement.
Citation
Lu, M., Wang, X., & Speeckaert, R. (2023). Price bubbles in Beijing carbon market and environmental policy announcement. Communications in Statistics - Simulation and Computation, 52(3), 884-897. https://doi.org/10.1080/03610918.2020.1870696
Journal Article Type | Article |
---|---|
Acceptance Date | Dec 23, 2020 |
Online Publication Date | Jan 20, 2021 |
Publication Date | 2023 |
Deposit Date | Feb 15, 2021 |
Publicly Available Date | Jan 20, 2022 |
Journal | Communications in Statistics - Simulation and Computation |
Print ISSN | 0361-0918 |
Electronic ISSN | 1532-4141 |
Publisher | Taylor and Francis Group |
Peer Reviewed | Peer Reviewed |
Volume | 52 |
Issue | 3 |
Pages | 884-897 |
DOI | https://doi.org/10.1080/03610918.2020.1870696 |
Public URL | https://durham-repository.worktribe.com/output/1252265 |
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Copyright Statement
This article has been accepted for publication in Communications in Statistics - Simulation and Computation, published by Taylor & Francis.
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